Oil & Gas Giant Cuts Costs with Data Automation

Overview

A global oil and gas (O&G) major struggled with manual data burdens that distracted rig personnel from critical operational tasks. By partnering with us to establish an automated, 24/7 Wells Data Center, the energy supermajor centralized operations, standardized global reporting, and achieved significant cost reductions. This transformation freed engineering teams to focus on safety and core drilling activities.

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Client Background and Challenges

This global energy supermajor spans upstream, downstream, and chemical segments, with a strategic focus on energy innovation and a carbon-neutral future. Despite their scale, the O&G major’s drilling operations faced severe data inefficiencies. Rig Supervisors and Engineers were spending approximately two hours daily on manual data entry rather than on critical oversight.

Our Approach and Solution

We implemented a Wells Data Center (WDC), a centralized 24/7 support model operating across four shifts to ensure continuous global oversight for the energy major. Combining human domain expertise with advanced automation, we transformed their data landscape:

Intelligent Automation

We deployed UiPath RPA bots to populate 18 distinct tables in the wells information management system, automating the processing of 350-450 attributes per rig daily.

Advanced Scripting

We utilized Python scripts for data analytics to handle complex data extraction, quality checks, and reusable data transfer.

Real-Time Monitoring

Our team established dashboards for real-time rig sensor monitoring and data quality management (DQM), ensuring immediate triage of low-latency rig data.

Standardization

We harmonized reference data and standardized global daily reporting templates to eliminate discrepancies between systems.

Business and Community Impact

  • $7.5 Million Saved: Achieved cumulative savings of approximately $7.5 million since 2016.
  • 95% Time Reduction: RPA bots reduced data entry time from 2-3 hours to under 10 minutes per rig/day.
  • 7,300 Hours Returned: Freed up ~7,300 hours annually for rig supervisors across 10 rigs.
  • 99% Data Quality: Maintained across 20+ data types and 200+ attributes.